For the study, the lawyers and the LawGeex AI had to analyse five previously unseen contracts with 153 paragraphs of technical legal language, under controlled conditions precisely prepared the way lawyers review and approve everyday contracts. The highest performing lawyer stood in line with LawGeex AI by achieving 94% accuracy but the average accuracy achieved by the least performing lawyer stood at just 67%. The most notable difference in the test between machines and humans lies in the time factor: while it took LawGeex AI only 26 seconds to complete the task, the lawyers took average of 92 minutes. The longest time spent by the humans to accomplish the test was 156 minutes and the shortest time recorded was 51 minutes. Commenting on the study, Gillian K. Hadfield, Professor of Law and Economics at the University of Southern California said: “This research shows technology can help solve two problems – both making contract management faster and more reliable, and freeing up resources so legal departments can focus on building the quality of their human legal teams.”
The notion that IT struggles to move beyond their traditional role and into a more innovative one is very common. But, as the IDC statistic shows, IT is more often a cost center, rather than a source of innovation and revenue for the company. Why is this situation still so widespread? A core issue is that nearly everything in the datacenter is manual and not automated. Most datacenters have custom configurations that require their own manual maintenance with specialized tools. Incremental progress on any one or two help, but it isn’t enough to substantially change the big picture for the company. Over time, people get used to this status quo and start to think that it is completely normal. They fall into the trap of believing that a huge step forward toward automation and innovation is impossible.
In February 2017, GitHub announced it was revising its terms of service and invited comments on the changes, several of which concerned rights in the user-uploaded content. The earlier GitHub terms included an agreement by the user to allow others to "view and fork" public repositories, as well as an indemnification provision protecting GitHub against third-party claims. The new terms added a license from the user to GitHub to allow it to store and serve content, a default "inbound=outbound" contributor license, and an agreement by the user to comply with third-party licenses covering uploaded content. While keeping the "view and fork" language, the new terms state that further rights can be granted by adopting an open source license. The terms also add a waiver of moral rights with a two-level fallback license, the second license granting GitHub permission to use content without attribution and to make reasonable adaptations "as necessary to render the website and provide the service."
Consider a case where a monitoring system is to detect anomalies within the data. Typically, one may turn into the classic means of outlier analysis like the DBSCAN-based approaches or LOF. Nothing wrong with these, they may perfectly well point towards the directions where the outliers may be present. However, these techniques may require substantial computational resources to complete the task on high volumes of data in reasonably acceptable amount of time. A much faster alternative may come from considering the given case as a time series analysis problem. Such data coming from a system operating in ‘healthy’ conditions would have a typical, acceptable amplitude distribution and, in such scenario, any deviation from the expected shape may be considered a potential threat, worth detecting. A very fast descriptive statistic aimed at summarizing the shape of the distribution of the signal is called the ‘kurtosis’.
Internet Service Providers will retain IP addresses of customers and the servers that they connect to. Armed with this information, forensic investigators can determine which websites suspects are accessing. However, most ISPs do not keep records of the actual content their subscribers access. There are a couple of reasons for this. First of all, keeping records of all content would be far more demanding on their servers. They simply don’t have the resources, even in the age of big data. Even if they wanted to keep these records, it would be impossible to see what content customers are accessing on most websites. Most websites have encrypted connections, so Internet Service Providers can’t tell what their users are doing on them. For example, since Facebook uses HTTPS connections, Internet service providers can’t read the customers’ messages or seewhat content they post on their Facebook feed. Nor can they see what they are searching for on Google.
Artificial intelligence (AI), machine learning (ML), and predictive analytics applications may one day prove to be the key to maintaining control and preventing successful hacks, data breaches, and network compromise. These technologies encompass deep learning, algorithms, and Big Data analysis to perform a variety of tasks. The main goal of AI and ML is usually to find anomalies in systems and networks of note, whether it be suspicious traffic, unauthorized insider behavior and threats, or indicators of compromise. Able to evolve over time, the purpose of AI technologies is to learn, detect, and prevent suspicious and dangerous activities with improvements and refinements the longer such applications and systems are in use. This provides companies with a custom cybersecurity system which tailors itself to their requirements, in comparison to an off-the-shelf, traditional antivirus security solution -- which is no longer enough with so many threats lurking at the perimeter.
This bolsters Gartner’s opinion that the most successful organisations are more likely to trust their IT organisation to manage their IT and digital technology spending. Respondents were also questioned about who manages the selection and approval of cost optimisation ideas. Those with visibility of both the IT shared services budget and all digital spending across the organisation reported that, on average, nearly half of their digital technology spending is paid for by the business. A quarter is paid for out of the IT budget, with chargeback to the business. “As you’d expect, CIOs have the most influence over the selection and approval of cost optimisation opportunities within IT shared services,” said Buchanan. “Interestingly, CIOs who focus on digital business opportunities have greater responsibility for cost optimisation than those who don’t. This suggests that CIOs are starting to exert influence over selecting and approving digital business ideas to optimise business costs.”
Medical technology tends to lag behind other technologies, as the cost of mistakes at medical practices and hospitals can be astronomical. As a result, the field can lag behind when it comes to adopting the latest digital or IoT technologies. Patient privacy is a major issue, so all new technologies must be adopted carefully while adhering to various data compliance obligations that apply both to companies in general as well as healthcare organisations specifically. Managing clinics and hospitals is complex, and it’s expensive. Many healthcare organizations rely on multiple computer and networking systems. Through smart bracelets, administrators can better track patient movement, and they can determine how often patients meet with their doctors. In addition, IoT technology can make it easier to track and analyze patients’ vital signs and other metrics, offering invaluable feedback and resolution not possible with manually measurements.
Digital industrial leadership is transforming the industrial world. For BHGE, specifically, data and analytics are fundamentally changing the way work gets done in our business and in the oil and gas industry as we prepare for the next big step-change in productivity. When I think of digital industrial leadership, I think about using data to move from looking in the rear-view mirror to looking into the future. We are beyond making decisions based on order history; we are using data to be predictive and make recommendations for future sales targets. As an example, we are using artificial intelligence on the shop floor to understand what drives disruptive, unscheduled downtime of our welding machines. When information technology meets operations technology, we learn what behaviors or indicators lead up to that unplanned downtime. We can use predictive analytics to do preventive maintenance and improve our productivity.
To predict the credit default, several methods have been created and proposed. The use of method depends on the complexity of banks and financial institutions, size and type of the loan. The commonly used method has been discrimination analysis. This method uses a score function that helps in decision making whereas some researchers have stated doubts on the validity of discriminates analysis because of its restrictive assumptions; normality and independence among variables . Artificial neural network models have created to overcome the shortcomings of other inefficient credit default models. The objective of this paper is to study the ability of neural network algorithms to tackle the problem of predicting credit default, that measures the creditworthiness of the loan application over a time period. Feed forward neural network algorithm is applied to a small dataset of residential mortgages applications of a bank to predict the credit default.
Quote for the day:
"Never allow someone to be your priority while allowing yourself to be their option." -- Mark Twain